A research critique published in JMIR Mental Health analyzed how researchers have utilized natural language processing (NLP) technologies to better understand bipolar illness and suggested a potential future for NLP implementation. NLP is a branch of AI, which involves providing computers the ability to interpret text and spoken language in a manner similar to that of human beings.
According to the review, 35 studies have been compiled to better describe the use of an NLP technique for bipolar disorder research. They used narrative synthesis to map the research in order to address four research questions. These queries concerned potential trends, NLP methodologies used, therapeutic and practical applications mentioned, and ethical difficulties discovered in the research.
Since 2015, there has been a significant interest growth in the topic, and has relatively remained constant. Publications on NLP and bipolar disorder have increased from one study in 2004 to five studies in 2020. Objectives for these studies can be further divided into four different categories:
- Prediction and classification
- Language characterization of bipolar disorder
- Measure health outcomes via EHRs (Electronic Health Records)
- Use EHRs for phenotyping
With prediction and classification being the important objective and language characterization of bipolar disorder being the second important objective.
The publications included in this study revealed a wide range of clinical and practical consequences of the research. One of the most prevalent uses mentioned was that utilizing NLP approaches with social media data might help with early diagnosis, clinical evaluation, and suicide prevention. It can also help in reducing social isolation, enhance coping skills, and boost patient comprehension of their condition.
This study’s findings might potentially be used to detect poor mental health and generate urgent alerts for targeted therapy. The data can also be utilized to give treatment to previously inaccessible populations via telemental care. The usage of NLP and EHRs may potentially result in the development of accurate diagnostic algorithms for bipolar disorder.
Patient privacy has always been a top priority in the medical field and here the researchers noted that applications of NLP approaches connected to bipolar disease, particularly those that employ social media data, require ethical considerations in order to protect patient privacy. Sixty percent of the research collected by the researchers contained ethical problems in some form, whereas the remaining forty percent did not. Until 2016, more than half of the researchers had minor ethical problems.
Paper published after 2017 onwards had more thorough explanations of ethical decision-making. Now the articles were required to include detailed discussions of both positive as well as negative social implications. According to the research cited, any research employing NLP to explore bipolar disorder should follow ethical artificial intelligence principles to reduce risk and preserve participant data and privacy.
The study determined that the benefits of NLP, particularly when combined with bipolar illness studies, indicated that language analysis might aid in the delivery of treatment for patients suffering from bipolar disease. Using natural language processing (NLP) to explore vulnerability, gender representation in bipolar disorder populations online, web-based services, and social and occupational functioning might help both the medical community and bipolar disorder patients.
Author- Toshank Bhardwaj, AI Content Creator
